About me
Hello! I got a Ph.D. in Computer Science from Georgetown University. I was fortunate to work with my advisor, Prof. Lisa Singh. My research focuses on Data Science, Social Media Mining and NLP.
Most of my projects were collaborations with CNN, Washington Post, UMich and MDI on leveraging AI/ML methods for data-centric social concerns such as sentiment analysis, stance detection, force-migration, gun policy, MeToo movement, spam, misinformation and fake news. I spent some time working at 
Twitter and 
Google.
News
- One workshop paper accepted at AAAI 2023 (to appear).
- I passed my PhD dissertation defense 🎉
- One paper accepted at ECML 2022 (to appear).
- I passed my PhD proposal defense 🎉
- Three papers accepted at Machine Learning, LREC 2022, and DATA 2022.
- I joined Twitter as an ML intern.
- Our paper has been accepted at
The Harvard Misinformation Review. - I served as a reviewer at EMNLP 2021, ACL 2022 and ICWSM 2023.
I joined
Google Cloud AI as a PhD intern.- Our paper has been accepted as a long paper at NAACL 2021.
- Our project with
known as The Breakthrough is reported weekly on CNN Politics. - Our paper has been published in the Journal of Computational Social Science.
- Our work about COVID-19 misinformation was reported on
Washington Post. - Started building this web page.
Interests
- AI, ML, Data Science
- Natural Language Processing
- Data-Efficient Learning
- Misinformation / Fake News
Education
-
PhD in Computer Science, 2017 - 2022
Georgetown University, USA
-
BEng in Computer Engineering, 2012 - 2016
Chulalongkorn University, Thailand
Skills
Python
Machine Learning
Data Science
Experience
Machine Learning Intern
- Leverage AI/ML to improve Twitter.
- Conduct and deploy end-to-end machine learning pipelines from research to production.
Software Engineer Intern (AI/ML)
- Research, experiment and implement new features for Google AI products used by millions of people around the world.
- Collaborate with Google AutoML, Google Cloud AI and Google Brain teams.
Research Intern
- Research in data science, NLP and social media mining focusing on misinformation and fake news in social media.
- Collaborate with researchers from CNN and University of Michigan to conduct and weekly report our analysis about the US election at The Breakthrough.
Teaching Assistant
- COSC-282 Big Data Analytics (Undergraduate Level - Spring 2018)
- COSC-287 Introduction to Data Science (Undergraduate Level - Fall 2019)
- COSC-587 Introduction to Data Analytics (Graduate Level - Fall 2021)
Research Assistant
- Advise student research groups working on social media mining projects.
- Conduct research funded by Massive Data Institute (MDI) and National Science Foundation (NSF).
Software Engineer
- Gather requirements, design, development, testing and validation using Ruby on Rails.
- Develop APIs to reduce back-office operation time by 75% including PDF bill generation, email confirmation, bank account notification for Slack, etc.
- Research in Neuroscience and Machine Learning mainly using MATLAB and C++. The advisor is Prof. Masayuki Numao.
- Collaboratively conduct experiments with the Biochemical Lab (Nagai Lab).
- Develop APIs to collect streaming data from EEG brainwave headset in C++ and apply ML models to evaluate user’s emotions.
Research Assistant
- A member of Machine Intelligence and Knowledge Discovery Lab (MIND Lab).
- Conducting research to solve data science problems in real-world including wind power prediction from power plants (time series), emotion prediction from brain wave (neuroscience and ML/AI) and analysis of course materials (text mining).
Publications
(2022).
Investigating Scientific Misinformation Using Different Modes of Learning.
In SDU@AAAI.
(2022).
DeMis: Data-efficient Misinformation Detection using Reinforcement Learning.
In ECML-PKDD.
(2022).
PoliBERTweet: A Pre-trained Language Model for Analyzing Political Content on Twitter.
In LREC.
(2022).
Inferring #MeToo Experience Tweets Using Classic and Neural Models.
In DATA.
(2022).
Traditional and Context-specific Spam Detection in Low Resource Settings.
In Machine Learning.
Related Courses
Graduate-level
- Algorithms
- Machine Learning
- Neural Networks and Deep Learning
- Text Mining & Analysis
- Intro to Data Analytics
- Massive Data Fundamentals
- Web Search and Sense Making (Scala)
- Dialogue Systems
- Algorithms for Distributed Machine Learning (Ph.D. Seminar)
- Distributed Algorithms and Systems (Ph.D. Seminar)
- Data Protection by Design (Ph.D. Seminar)
- Deep Reinforcement Learning (Ph.D. Sit-in)
Undergraduate-level
- Algorithm Design
- Artificial Intelligence
- Software Engineering
- Auto Speech Recognition
- Time Series Mining
- Web Development
- Statistics for Physical Science
- Data Structure (C++)
- Programming Method (JAVA)
- Distributed System Essential
- System Analysis and Design
- Computer Security
- Database Design
- Computer Network
- Computer Graphic
Contact
- kornraphop.k [at] gee-mail